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BMC Genomics. 2016 Sep 26;17(1):754.

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons.

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Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.
Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Canada.
Department of Molecular Medicine, Université Laval, Québec, Canada.
Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.
Big Data Research Centre, Université Laval, Québec, Canada.
Division of Infectious Diseases, Departments of Medicine and Microbiology, McGill University Health Centre, Montréal, Canada.
Department of Medicine, McGill University, Montréal, Canada.



The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies.


The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery.


Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes ( ).


Antibiotic resistance; Bacteria; Biomarker discovery; Genomics; Machine learning

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